The viral AI-footprint numbers all describe one query or one run, and that is the wrong scale

Strubell put a carbon number on training one model

In 2019 Emma Strubell and two colleagues at the University of Massachusetts Amherst measured what the field had mostly guessed at. They sampled the real power draw of the processors training four standard language models. They converted that to carbon using a United States grid average of 0.954 pounds of CO2e per kilowatt-hour, together with a data-centre overhead factor of 1.58. Training one large Transformer worked out to 192 pounds of CO2e, and their BERT model came to about the carbon of a single passenger flying across the country.¹

That paper handed the environmental case its first hard number, and the case has grown from it. If one run costs that much, and the models keep getting larger and more numerous, then the warehouses of chips training and serving them become an electricity and water problem worth taking seriously. The strongest version of the argument no longer rests on any single run. It points at the build-out itself, the data centres going up to run these models, and at the water evaporated to cool them, which almost no operator reports. One row in Strubell's own table, comparing a run against five car-lifetimes, became the image that carried the alarm into the press.¹

How the three-watt-hour query shrank to 0.3

The number most people have actually heard is not about training but about a single question. A widely repeated figure held that one ChatGPT query uses roughly 3 watt-hours, ten times a Google search. How that per-query energy is measured is a subject of its own, worked through in a companion lesson on energy per query; what matters here is where the 3 came from and where it went. It traces to Alex de Vries's 2023 analysis, which built on a February 2023 estimate that assumed 4,000 input and 2,000 output tokens running on Nvidia A100 chips.²

In February 2025 Epoch AI recomputed a typical GPT-4o query at about 0.3 watt-hours, roughly ten times lower.² Three assumptions had inflated the old figure. It counted a far longer answer than a real query returns, around 500 output tokens rather than 2,000. It assumed the older A100 rather than the newer H100. And it used peak power, not the power a chip actually draws. Reporting at the time recorded the walk-back.³

The correction is not that a query is always trivial. Epoch's own numbers climb to about 2.4 watt-hours for a 10,000-token input and about 40 for 100,000. The IEA's measured per-task figures run wider still: 0.3 watt-hours on a small text model, 5 on a medium one, 1.7 for an image, and 115 for a six-second video, against roughly 15 to charge a phone.⁴ A short text question is a rounding error next to charging your phone. A generated video is not. Ask the same question about water and there is nothing to correct, because there is no reliable per-query figure to begin with. The most-quoted water numbers trace to work no primary document here could confirm, and the operators who could measure it publish almost nothing. Water is the part of the per-query story that is a genuine blank.

The five-car figure was a model search, corrected 88-fold

Strubell's table put a full neural architecture search, an automated hunt across 979 million training steps for a better model design, at 626,155 pounds of CO2e. The row beneath it, one car's lifetime emissions, was 126,000 pounds.¹ That is the origin of "training a model equals five cars," and it describes the search, not a training run. A single Transformer in the same table is 192 pounds. Patterson and colleagues later cut the search estimate 88-fold, to about 3.2 tonnes.⁵ The original assumed the search ran at full scale when it in fact ran on a small proxy task, and it did not credit the efficient data centres that handled the rest. A search like that is also run once for a whole class of problems, not once per model, so its cost was charged to the wrong line.

The number for an ordinary run is real, but it moves by more than an order of magnitude on where and how you count. Patterson put GPT-3's training at 552 tonnes on a United States average grid.⁵ BLOOM, a model of comparable size trained on France's nuclear-heavy grid at 57 grams per kilowatt-hour, came to about 25 tonnes counting only the power the run drew.⁶ Widen BLOOM's boundary to take in the manufacture of the hardware and the idle infrastructure around it and the same run doubles, to 50.5 tonnes.⁶ A carbon figure for one training run says nothing without the grid it ran on and the boundary it counts.

Data centres drew 415 terawatt-hours in 2024

This is the half that is measured rather than extrapolated. The IEA's 2025 Energy and AI report puts data-centre electricity at about 415 terawatt-hours in 2024, roughly 1.5 percent of world electricity, growing about 12 percent a year since 2017, four times faster than total demand. Its Base Case projects about 945 terawatt-hours by 2030, a little more than Japan's entire electricity use today.⁴ That projection is a scenario, not a measurement: the report's four cases span roughly 670 to 1,260 terawatt-hours at 2030 and 700 to 1,720 by 2035. The base year is counted; the doubling is a band.

Even the base year depends on what you fold in. An earlier IEA edition reported about 460 terawatt-hours in 2022 and floated more than 1,000 by 2026, but it counted cryptocurrency mining in the same total. The 2025 figure is lower because it sets crypto aside, not because data centres shrank.⁷ The history also warns against straight-line panic. Masanet and colleagues found global data-centre energy rose only about 6 percent from 2010 to 2020, even as the computing done inside them rose roughly 550 percent.⁵ Each unit of work kept getting cheaper to run. Per unit, the trend has been strongly downward. In total, the load has still climbed fast. Both hold at once.

Where the alarm and the dismissal both overreach

The people making the environmental case today want disclosure of energy and water use, efficiency standards, and, in the strongest version, a slower build-out. De Vries, whose 2023 paper seeded the per-query figure, projected AI electricity rising 85 to 134 terawatt-hours a year by 2027, with a worst case in which Google's AI alone draws about as much as Ireland. Those figures are reported here from secondary coverage, because the paper itself sits behind a bot check.⁸

Each side reaches past its evidence. The alarmist version quotes per-query figures with no sourcing and skips over how fast the cost per query has fallen. The dismissive version waves away a real, fast-growing aggregate load and the undisclosed water by pointing to how little one query costs. They are answering different questions. Whether a single query is cheap tells you nothing about whether a cluster of new data centres strains a regional grid, and the measured aggregate tells you nothing about which query you should feel bad about. What would settle it is disclosure the operators can produce and mostly do not: how much electricity and water each facility uses, and which grid its power is drawn from.